3f600018f9ce132783e7e21c22fd1528b08204f4,pyglmnet/pyglmnet.py,GLM,predict_proba,#GLM#Any#,911

Before Change

        yhat = _lmb(self.distr, self.beta0_, self.beta_, X, self.eta,
                    fit_intercept=True)
        yhat = np.asarray(yhat)
        return yhat

    def fit_predict(self, X, y):
        Fit the model and predict on the same data.

After Change

        X = check_array(X, accept_sparse=False)
        check_is_fitted(self, "is_fitted_")

        if self.distr in ["binomial", "probit"]:
            return self._predict_proba(X)
        else:
            warnings.warn("This is only applicable for \
                           the binomial distribution. \
                           We returns predict as an output here.")
            return self.predict(X)

    def fit_predict(self, X, y):
        Fit the model and predict on the same data.

        Parameters

Instances